Explaining automatic recommendations is an active area of research since it has shown an important effect on users’ acceptance over the items recommended. However, there is a lack of research in explaining content-based recommendations of images based onvisual features. In this paper, we aim to fill this gap by testing threedi‚erent interfaces (one baseline and two novel explanation interfaces) for artistic image recommendation. Our experiments with N=121 users con€rm that explanations of recommendations in the image domain are useful and increase user satisfaction, perception of explainability, relevance, and diversity. Furthermore, our experiments show that the results are also dependent on the underlying recommendation algorithm used. We tested the interfaces with two algorithms: Deep Neural Networks (DNN), with high accuracy but with difficult to explain features, and the more explainable method based on Attractiveness Visual Features (AVF). The better the accuracy performance –in our case the DNN method– the stronger the positive effect of the explainable interface. Notably, the explainable features of the AVF method increased the perception of explainability but did not increase the perception of trust, unlike DNN, which improved both dimensions. These results indicate that algorithms in conjunction with interfaces play a signifcant role in the perception of explainability and trust for image recommendation. We plan to further investigate the relationship between interface explainability and algorithmic performance in recommender systems.